Metabolic monitoring, a method and apparatus for indicating a health-related condition of a subject

ABSTRACT

An apparatus for indicating a health-related condition of a subject has an input interface for receiving a sequence of samples of a first biological quantity derived by a first measurement method, the first measurement method being an invasive measurement and having a first impact on the subject, and for receiving a sequence of samples of a second biological quantity derived by a second measurement method, the second measurement method being a non-invasive measurement and having a second impact on the subject, wherein the first biological quantity gives a more accurate indication of the health-related condition of the subject than the second biological quantity, wherein the first biological quantity and the second biological quantity have a correlation to the health-related condition of the subject, and wherein the second impact is smaller than the first impact; a predictor for providing, for a certain time, for which no sample for the first biological quantity exists, an estimated value of the first biological quantity using samples for the first biological quantity and, as far as available, samples for the second quantity; and an output interface for outputting the estimated value or data derived from the estimated value so that an indication for the health-related condition of the subject is obtained.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/842,831, filed Jul. 23, 2010, which is currently allowed, which is adivisional of U.S. patent application Ser. No. 11/467,476, now U.S. Pat.No. 7,878,975, filed on Aug. 25, 2006, which is a continuation ofInternational. Application No. PCT/EP2005/002006, filed Feb. 25, 2005,which designated the United States, and was not published in English,which claims priority to Swedish Patent Application No. 0400456-0, filedon Feb. 26, 2004 and Swedish Patent Application No. 0402139-0, filed onSep. 7, 2004, each of which is incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to improved interpretation of noisyphysiologic and biochemical signals by the use of filtering, predictionand trend analysis of patient data, and discloses a method and deviceand/or a computer program product that aims at improving motivation,self-control and self-management of patients having type 2-diabetes ordiabetes-related disease. The invention monitors oxygen utilisation ofthe heart, thus physical condition and fitness, and indicates stimulantsand drug abuse and psychological and emotional stress. The inventiondiscloses the use of a painless, non-invasive surrogate measure forblood glucose, as well as blood glucose prediction by sparse bloodsampling, and a metabolic performance indicator. The invention offerslong-term, metabolic monitoring at low cost combined with ease of use,and creates patient awareness of metabolic system function relating tothe disease in an intuitive way, needing very little effort by the user.Lower cost, a lower burden for the health care system, prolongedlifespan and increased quality of life for the patient may be gainedfrom the use of the proposed invention.

2. Description of Prior Art

Physiologic and biochemical signals for example blood glucose sampling,blood pressure and other monitored signals of mammalians can be verynoisy, having a high variance when sampled over time. It is thereforecritical to reduce such noise before accurate interpretation of the datacan be made. Further, biochemical signals are often invasive in natureand such measurements can be discomfortable, costly or complicated toapply. The proposed invention strives to improve accuracy ininterpretation of such signals by the use of suitable filtering methodsand to reduce discomfort and cost by the use of non-invasive surrogatemeasures.

Diabetes is increasing globally in epidemic proportions and stands for amassive cost burden of healthcare. Type 1-diabetes, stands for around10% of all diabetes cases. Type 2-diabetes, therefore stands for around90% of all diabetes cases, and is steadily increasing. In the UnitedStates alone, it is estimated that up to 7% of the population may havediabetes. 100 million individuals are overweight, thus at high risk fortype 2-diabetes. If this trend continues, 100% of the US adultpopulation will be obese in year 2030. Total yearly cost of diabetes inthe US including indirect costs where 1997 estimated to approximate USD100 billion. In Saudi Arabia it has been estimated that up to 25% of thepopulation may have diabetes related disease. The World HealthOrganization (WHO) predicts an increase to 300 million diabetes patientsworldwide by the year 2025. Various attempts have been made to reversethis global epidemic trend, but to date this has failed.

Type 1-diabetes, (earlier referred to as insulin dependent diabetesmellitus IDDM), is identified by irreversible beta-cell destruction,that usually results in absolute insulin deficiency. Type 2-diabetes,(earlier referred to as non-insulin dependent diabetes mellitus, NIDDM),is identified as a heterogeneous disorder believed to involve bothgenetic and environmental factors. Type 2-diabetes is to a great extenta lifestyle related disease where modern sedentary lifestyle incombination with poor eating habits is believed to be major sources ofthe problem. The type 2-diabetes patient typically does not requireinsulin treatment for survival. The typical symptoms of type 2-diabetesare: Thirst, frequent urination, drowsiness, fatigue, overweight,gustatory sweating, varying blurred vision, elevated blood sugar levels,acetone breath and sugar in the urine. An examination of the patientwill quite typically reveal a sedentary lifestyle and a distinctpreference for a diet high in saturated fats and refined carbohydrates.

Insulin resistance is a common metabolic abnormality that characterizesindividuals with various medical disorders including type 2-diabetes andobesity and occurs in association with many cardiovascular and metabolicabnormalities. Insulin resistance is defined as the inability of thebody to respond adequately to insulin. The Syndrome-X or MetabolicSyndrome, also named the Insulin Resistance Syndrome, is a cluster ofmetabolic and physiologic risk factors that predict the development oftype 2-diabetes and related cardiovascular diseases. It is generallycharacterized by five major abnormalities; obesity, hypertension,insulin resistance, glucose intolerance and dyslipedaemia. Theprevalence rate of the metabolic syndrome in western countries is25-35%. Aging is generally associated with insulin resistance anddeteriorating beta cell function and obesity with insulin resistance andhyperinsulinemia.

Diabetic autonomic neuropathy (DAN) is a serious and one of the mostcommon complications of diabetes. Most type 2-diabetes patients die incardiovascular diseases preceded by a deterioration of the functionalityof the autonomic nervous system (ANS). This is seldom noticed at anearly stage, making type 2-diabetes a “stealth” disease developingslowly over the years and is most often unnoticed by the patient untildiscovered at a late stage. DAN impairs the ability to conduct normalactivities of daily living, lowers quality of life, and increases therisk of death. DAN affects many organ systems throughout the body e.g.,gastrointestinal, genitourinary, and cardiovascular. DAN is a result ofnerve fibre destruction and loss related to the “toxic” effects ofelevated blood glucose levels. Intensive glycaemic control is thereforecritical in preventing the onset and slowing the progression of DAN. ANSproblems and DAN can successfully be detected by the assessment of heartrate variability (HRV) analysis.

Hypertension is a major health problem in the western population andassociated to cardiovascular disease. Arterial stiffening may be both acause and consequence of hypertension, however recent research suggeststhat arterial stiffening is the typical precursor to hypertension, andthat arterial stiffening is likely to have a genetic basis. The majorityof type 2-diabetes patients (over 50%) suffer from hypertension. It istherefore imperative to control the blood pressure of diabetic patients.In type 2-diabetes it is recommended to keep the blood pressure below130/80 either by improving life-style or by medication or a combinationof both.

Insulin resistance and type 2-diabetes are associated with changes inplasma lipoprotein levels. Up to 70% of patients with type 2-diabeteshave lipid disorders. Coronary heart disease is the leading cause ofdeath among patients with type 2-diabetes. Dyslipidemia, together withobesity, hypertension, and hyperglycemia contribute strongly to coronaryheart disease. Even mild degrees of dyslipidemia may elevate coronaryheart disease risk factors. As these risk factors are additive or evenmultiplicative, strategies for lifestyle improvement should not onlyfocus on hyperglycemia but also on dyslipidemia. As dyslipidemia in type2-diabetes usually show smaller and denser LDL-particles, which are moreatherogenic, the target for cholesterol lowering should includevery-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) aswell as lowering of elevated triglycerides (TG).

Mental stress, elevated blood-pressure and elevated heart rate arecommon problems of today's society. Modern work and lifestyle is lessphysically active where hi-tech related jobs often result in a sedentarylifestyle. High demand work with sustained high levels of stress iscommon and a negative effort/reward factor can contribute to stressinduced disease. It is well known that mental stress can influencemetabolism such as elevated blood-glucose levels as well as an increasedsystolic blood pressure and heart rate. Various stimulants such ascaffeine, nicotine, alcohol, cocaine and amphetamine also increasesystolic blood pressure and heart rate.

Modern type of diet, high in energy and fat content is associated withinsulin resistance and related disorders. The exact aetiology of insulinresistance is however not clear. Genetic predisposition andenvironmental factors including quality and quantity of dietary fat,both contribute to development of an inability to adequately disposeplasma glucose at normal plasma insulin levels. Fast food outlets aregaining popularity due to high sugar, fat-rich and tasty food incombination with time-efficient eating. The increased consumption offast-acting, high-energy carbon hydrates reflects in blood sugarovershoots and insulin overshoots followed by blood sugar undershootsand drowsiness, again demanding renewed intake of fast-acting carbonhydrates etc. This cyclic feedback is frequently pounding the metabolicregulatory system. Such transient excitation is believed in the longterm to be harmful and contribute to insulin resistance and elevatedinsulin levels, the early start-up of the type 2-diabetes process. Theabove life-style related problems are currently creating health problemsof a magnitude unheard of in the past.

Physical activity, thus aerobic fitness is the cornerstone in fightingtype 2-diabetes related disease. It is a most important task to improvecardiovascular fitness by physical activity that increases thecapability and efficiency of the heart to supply the cardiovascularsystem with oxygen as well as improve insulin sensitivity and oxygenuptake of the muscles. The heart functions like any muscle that it canbe trained to become stronger and more efficient. A weight reduction byonly 10% usually shows positive effects on blood glucose and lipidlevels. In particular, it is important to reduce abdominal fat mass.

Physical activity and energy expenditure can be estimated in a varietyof ways that do not constrain the patient during his normal dailyactivities. Different methods exist like pedometers, accelerometers,heart-rate meters etc. One popular method use a pedometer to calculatenumber of steps walked or approximate the calories so consumed by asimple formula. Others calculate energy expenditure in relation to bodymovement and acceleration by the use of single-axial, bi-axial ortri-axial accelerometers. Another method use pulse monitoring based onplethysmograps, (a device that shines light through a finger or earlobeto calculate heart rate and physical activity). One other populardevice, a pulse watch, measures the EKG signal by the use of achest-strap with electrodes and transmits the EKG pulses to a speciallydesigned wrist-watch calculator, which can calculate calories consumedand other parameters related to physical activity. However the simplestway to quantify physical activity is to just roughly estimate the dailyactivity, for example on a scale from one to five, relating to the dailyeffort made and the intensity and duration of the physical activityperformed. More elaborate calculation and reporting methods include theMET tables (metabolic equivalent) or formula, which is an accurate indexof the intensity of physical activity. Modern inactive and sedentarylifestyle has opened up a large market for health gymnasiums andmarketing of various health-related products, and physical trainingprograms for the improvement of physical fitness. Despite this positivetrend, type 2-diabetes related disease is rapidly increasing at analarming rate.

It is difficult to motivate high-risk, overweight, sedentary anddiabetes-prone individuals to change life style. Just informing theindividual of the health-risks involved and the need of physicaltraining and/or the need for corrected eating habits and/or de-stressingtreatment is often not sufficient. Low fit individuals often do not feelcomfortable by being examined by somebody else or being forced toexercise training in gymnasiums. It is common to find overweightindividuals embarrassed by their low physical fitness level, and inorder to avoid humiliation, refuse to join rehabilitation programmes. Itis believed by the inventor that the only way to break such detrimentaltrend is to educate people by hands-on experience by the use of simpleand intuitive tools to monitor their own metabolic function, preferablyat home in private. The individual can then himself gain understandingof the problems involved and gain insight to what extent and intensityit is necessary to change lifestyle.

Self monitoring using a personal blood-glucose meter is usuallynecessary for type 1 insulin dependent diabetes mellitus (IDDM) patientsin order to aid self-administration of insulin. However it is lesscommon that blood-glucose monitoring is prescribed for patients withmanifest or borderline type 2-diabetes. Self-monitoring using urinedipsticks for urine-glucose measurements are more or less obsolete todayand seldom used due to the fact that the renal threshold variesindividually over a wide range. In addition this method cannot measureglucose levels below the renal threshold, exhibits long delay and lowsensitivity, and therefore the use of blood-glucose monitoring ispreferred.

Recent research has reported some benefits of using a blood-glucosemeter for BG-monitoring in connection with meals for patients with type2-diabetes. The idea is to monitor pre-prandial and post-prandialglucose levels to gain knowledge of the metabolic effect of the meal onthe patient. The patient can then learn by experience how the glucoselevel will raise post-prandially and give him feedback on the glucosevariation relating to different types of food intake. The idea is tobalance the food intake, where a reduction in refined fast actingcarbohydrates will reduce post-prandial blood-glucose overshoots. Suchovershoots are understood to cause long-term damage to the autonomicnervous system and eventually may lead to diabetes and diabeticneuropathy. Such form of self-monitoring is cumbersome and impracticalto maintain and it is not uncommon that patients drop out of such testtrials due to lack of motivation relating to the intensity of themethod. Blood glucose meters and tools need to be carried around by thepatient during the day and testing is sometimes disclosed in public whenhaving a meal in a restaurant. Including such cumbersome procedures aspart of a patient's long-term daily practice is not very likely tosucceed. In addition the cost is not negligible according to theconsumption of a number of blood-glucose sticks and a number offinger-puncturing lances during the day. In addition, although such testis minimally invasive in nature it can be painful and very uncomfortableto the patient. Further it gives little room for logic and intuitiveinterpretation of the results and it is therefore difficult tocomprehend and administer for the patient in order to obtain atherapeutic goal, a serious disadvantage.

The World Health Organisation (WHO) and American Diabetes Association(ADA) has specified blood-glucose ranges and levels in order todifferentiate between the different stages of diabetes. Fasting glucoseconcentrations that diagnose the symptomatic patient (WHO criteria,1999) are shown below. Fasting sample glucose concentrations are inmmol/L:

Whole Blood Plasma Venous Capillary Venous Capillary Manifest DiabetesMellitus >6.1 >6.1 >7.0 >7.0 Impaired glucose tolerance <6.1 <6.1 <7.0<7.0 (IGT) Impaired fasting glucose 5.6-6.0 5.6-6.0 6.1-6.9 6.1-6.9(IFG) Normal <5.5 <5.5 <6.0 <6.0

When assessing blood-glucose levels in the clinic, it is unfortunatelyquite common to overlook the existence of a strong biologic variabilityas well as an analytic variability. Thus substantial variability existsbetween observations that may be misinterpreted by the inexperiencedphysician resulting in reduced accuracy in grading and diagnosis of thedisease.

When a blood sample is drawn in a clinic, a number of factors influencethe accuracy of the measurement result such as:

-   -   1. Sub-optimum calibration of the clinical analysis instrument.        See a practical example in FIG. 1.    -   2. Aging of the blood sample by glycolysis, as glucose        preservatives does not totally prevent glycolysis.    -   3. “White-Coat Hyperglycemia”, elevated BG value due to a        nervous “needle-phobia” patient. See a practical example in FIG.        2.    -   4. A continuously falling fasting BG value, related to        increasing time of day.    -   5. A time-variable insulin sensitivity, thus different        sensitivity from day to day.    -   6. Female cyclic hormonal changes due to menstruation.    -   7. BG can vary due to transitory acute infections, traumatic        stress and even a simple cold or flu.

Relating to the above uncertainties, it is believed by the inventor thatblood-glucose monitoring under controlled conditions in the home, usinga sufficiently accurate blood-glucose meter together with suitablepost-processing and filtering methods, improves the accuracy of thediagnostic classification. This is believed by the inventor, to besuperior compared to established clinical laboratory measurements andcurrent praxis.

Although elevated insulin levels (hyperinsulinemia) appears in thebloodstream long before elevated blood glucose levels eventuallymanifest; yet a high glucose level remains the classic type 2-diabetessymptom classifier. Insulin levels are rarely, if ever, used as adiabetic risk marker or diagnostic tool except for clinical researchpurposes, a remarkable fact. Thus, a low blood glucose level does notpreclude the presence of the disease.

Monitoring of oxygen saturation is common practice of patients underemergency treatments as well as in the operating theatre. Before theinvention of the now widely used pulse-oximeter, (an instrument thatmonitors blood haemoglobin oxygen saturation using infrared lightabsorption), it was common practice to calculate theRate-Pressure-Product (RPP) of the patient during surgery to establishthe patients heart condition and oxygen utilization. The RPP (alsocalled the Double Product) is a reasonably accurate measure of heartoxygen utilization and is derived by multiplying the systolic bloodpressure by the heart rate (RPP=sBP×HR/100). After the introduction ofthe pulse oximeter, RPP has found little use today, but has some use insports medicine indicating oxygen consumption of the heart duringtreadmill exercise tests etc. RPP also indicate stress and the use ofstimulating drugs.

In order to ease the burden for the patient, the inventor declares thatonly fasting blood glucose sampling is necessary for accurate long termmonitoring and treatment of type 2-diabetes related disease. Evensparsely sampled blood glucose measurements for example once a week maybe sufficient, relating to an embodiment of the invention for anaccurate prediction of daily BG. More intensive and cumbersome bloodglucose monitoring like pre- and postprandial blood glucose measurementsduring the day is not deemed necessary as the fasting blood glucoselevel generally indicates the relative magnitude of postprandial bloodglucose excursions. Thus a higher fasting blood glucose level isreflected in a higher postprandial blood glucose level and vice versa.This can be indicated by the use of multiple three-sample oral glucosetolerance OGTT tests sampled at 0 h, 1 h and 2 h during an interventionlifestyle improvement period see FIG. 3. It can be seen that aslife-style is improved with lower fasting BG, also the postprandialBG-values follows the declining trend. However, 1 h post-prandial BGmeasurements may of course be used as an alternative to fasting BG whendeemed necessary. This is however more cumbersome and therefore lesspractical as explained above.

In an additional embodiment of the invention, the BG level is predictedfrom preferably blood pressure and heart rate (Rate Pressure Product)alone, making painful finger pricking or painful invasive proceduresunnecessary except for the initial calibration and set-up procedure ofthe predictor. In another embodiment of the invention, it offers lessfrequent need of painful finger pricking.

The proposed invention offers the patient an intuitive way to measureand analyse certain physiologic parameters such as for example intensityof physical activity, blood-glucose, blood pressure and heart rate. Inaddition important patient data such as lipid levels, total cholesterol,triglycerides, body temperature, weight, body mass index and the waistto hip ratio can be stored and processed. Following such measurements,data is processed and optimised using suitable filtering algorithms, andthereafter indicated to the patient in an intuitive manner for instantfeedback of his behaviour, progress and results.

A preferred embodiment of the invention comprises the following steps:

Estimating or measuring the level of physical activity on a preferablydaily basis, and preferably collecting this information into a database.

Measuring the fasting and/or post-prandial blood-glucose level on a moreor less frequent basis, densely or sparsely sampled, and preferablycollecting this information into a database.

Measuring the systolic-diastolic and heart rate on a frequent basis,densely sampled, and preferably collecting this information into adatabase

Calculate the rate pressure product from systolic blood pressure andheart rate.

Measure any other relevant physiological parameter such as body weight,body temperature, blood lipids etc and preferably collecting thisinformation into a database

Low-pass filter, enhance, error-correct and missing data-interpolate theabove data using statistical and/or signal processing methods.

Apply prediction methods to predict blood glucose values from preferablythe rate pressure product.

Combine and/or filter obtained data by suitable algorithms tonoise-reduce, clarify and improve the so obtained information forpresentation.

Present the processed, enhanced and/or predicted data as a trend to thepatient in an intuitive and easy to understand manner for easyinterpretation of patient parameters.

From the above, it becomes clear that metabolic monitoring of diabetesrelated disease is essential in order to assess at least the currentstatus of a subject. Dense sampling of vital biological parametersoffers several advantages. The main advantage is that the subject iscontinuously made aware of his current status so his health conditiondoes not deteriorate. An other advantage is that the subjectcontinuously receives an overview over any changes or trends in hiscurrent status, which may for example relate to a lack of physicalactivity or a lack of good nutrition in the worse case, or sufficientphysical activity and a well-controlled diet in the better case. Yetanother important advantage is that the subject gets instantaneousfeedback of his status and can adjust his lifestyle according to thedeveloping trend. The prerequisite for efficient metabolic monitoringaccording to the invention is that the subject monitors vital biologicparameters. For example blood glucose levels, blood pressure and heartrate can be measured at wake-up in the morning and physical activity canbe measured during the day etc.

Accurate blood glucose monitoring requires invasive measurements,although finger pricking may be considered as minimally invasive.Currently there is no other method that can compare in accuracy to aninvasive measurement. A subject pricks his finger to sample a smallamount of blood, which is subsequently examined in an analytical device,which outputs a blood glucose value. Even minimally invasive methods arecostly, and often experienced as discomfortable and can thus have anegative impact on the patient and disease management.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an improved conceptfor indicating a health-related condition of a subject, which is easierto comprehend, offers lower running cost, is more comfortable and moremotivating for the subject to use, compared to traditional methods.

In accordance with a first aspect, the invention provides an apparatusfor indicating a health-related condition of a subject, having:

an input interface for receiving a raw sequence of samples of a firstbiological quantity derived by a first measurement method, the firstmeasurement method being an invasive measurement and having a firstimpact on the subject, and for receiving a raw sequence of samples of asecond biological quantity derived by a second measurement method, thesecond being a non-invasive measurement and having a second impact onthe subject, the biological quantities having a useful variation and anon-useful variation;

wherein the first biological quantity gives a more accurate indicationof the health-related condition of the subject than the secondbiological quantity, wherein the first biological quantity and thesecond biological quantity have a correlation to the health-relatedcondition of the subject, and wherein the second impact is smaller thanthe first impact;

a predictor for providing, for a certain time, for which no sample forthe first biological quantity exists, an estimated value of the firstbiological quantity as a predicted sample using samples for the secondbiological quantity and, as far as available, samples for the firstquantity;

a filter for filtering a sequence having samples of the first biologicalquantity and at least one predicted sample, the filtered sequence havinga useful variation and a reduced non-useful variation compared to thesequence before filtering, and an output interface for outputting atleast an increase indication, a decrease indication or a remainunchanged indication as a trend of the data, the trend beingrepresentative to a useful variation of the health-related condition ofthe subject.

In accordance with a second aspect, the invention provides a method ofindicating a health-related condition of a subject, the method includingthe steps of:

receiving a raw sequence of samples of a first biological quantityderived by a first measurement method, the first measurement methodbeing an invasive measurement and having a first impact on the subject,the first biological quantity having a useful variation and a non-usefulvariation;

receiving a raw sequence of samples of a second biological quantityderived by a second measurement method, the second measurement methodbeing a non-invasive measurement and having a second impact on thesubject, the second biological quantity having a useful variation and anon-useful variation;

wherein the first biological quantity gives a more accurate indicationof the health-related condition of the subject than the secondbiological quantity, wherein the first biological quantity and thesecond biological quantity have a correlation to the health-relatedcondition of the subject, and wherein the second impact is smaller thanthe first impact;

providing by prediction, for a certain time, for which no sample for thefirst biological quantity exists, an estimated value of the firstbiological quantity as a predicted sample using samples for the secondbiological quantity and, as far as available, samples for the firstquantity; and

filtering a sequence having samples of the first biological quantity andat least one predicted sample, the filtered sequence having a usefulvariation and a reduced non-useful variation compared to the sequencebefore filtering; and

outputting at least an increase indication, a decrease indication or aremain unchanged indication as a trend of the data, the trend beingrepresentative to a useful variation of the health-related condition ofthe subject.

In accordance with a third aspect, the invention provides a computerprogram having a program code for performing a method of indicating ahealth-related condition of a subject, the method including the stepsof:

-   -   receiving a raw sequence of samples of a first biological        quantity derived by a first measurement method, the first        measurement method being an invasive measurement and having a        first impact on the subject, the first biological quantity        having a useful variation and a non-useful variation;    -   receiving a raw sequence of samples of a second biological        quantity derived by a second measurement method, the second        measurement method being a non-invasive measurement and having a        second impact on the subject, the second biological quantity        having a useful variation and a non-useful variation;    -   wherein the first biological quantity gives a more accurate        indication of the health-related condition of the subject than        the second biological quantity, wherein the first biological        quantity and the second biological quantity have a correlation        to the health-related condition of the subject, and wherein the        second impact is smaller than the first impact;    -   providing by prediction, for a certain time, for which no sample        for the first biological quantity exists, an estimated value of        the first biological quantity as a predicted sample using        samples for the second biological quantity and, as far as        available, samples for the first quantity; and    -   filtering a sequence having samples of the first biological        quantity and at least one predicted sample, the filtered        sequence having a useful variation and a reduced non-useful        variation compared to the sequence before filtering; and    -   outputting at least an increase indication, a decrease        indication or a remain unchanged indication as a trend of the        data, the trend being representative to a useful variation of        the health-related condition of the subject,

when running on a computer.

The present invention strives to reduce discomfort and cost for the userby the introduction of new surrogate measures and prediction.

The present invention is based on the finding that a high accuracyinvasive measurement method can be partly substituted by a surrogatenon-invasive measurement method. The high accuracy invasive measurementmethod is typically represented by a costly, uncomfortable and “hard”measurement method, and where the non-invasive measurement method is alow-cost, comfortable and “soft” measurement method, relating to itsimpact on the subject.

The predictor generates densely sampled invasive data, based on sparselysampled invasive data and densely sampled non-invasive data. Thus, thesubject does not have to commit to painful finger pricking daily or asoften as would be necessary in the prior art, but could revert to lessfrequent finger pricking as for example on a weekly basis. The subjectonly has to perform a simple and painless non-invasive blood pressurerelated measurement method on a for example daily basis, and thistherefore does not have a large impact on the subject.

In another preferred embodiment, the predictor is fed with more than onebiological quantity, which is derived from a non-invasive measurement.

In accordance with the present invention, the only prerequisite for thetwo measurements or biological quantities is that both measurements havea correlation to the health-related condition of the subject.

Further, the present invention strives to improve accuracy in theinterpretation of noisy physiologic signals by the use of low-passfiltering methods for extraction of the useful signal variation andremoval of non-useful signal variations.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects and features of the present invention willbecome clear from the following description taken in conjunction withthe accompanying drawing, in which:

FIG. 1 shows fasting BG-measurements from two different occasions andclinics. Each occasion compares a lab-measurement with the mean of threemeasurements from three high quality BG-meters of the same brand. (Bar1, 2 and 4 are expected to be correct).

FIG. 2 shows the impact of “needle phobia”, on three different testoccasions, where the BG-value is rising substantially when the nurse isusing a needle. The measurements are mean-values of three high qualityBG-meters of the same brand.

FIG. 3 shows three OGTT's from three different occasions. As few as onlythree samples can describe the BG-dynamics well.

FIG. 4 shows raw fasting BG-measurements provided by the Case-Study(dots) together with the trend (low-pass filtered signal). WHO-limitsare also presented.

FIG. 5 shows that according to WHO-limits, there is a strong uncertaintyin typical clinical BG-measurements as the diagnosis of the patient isvery dependent on the occasion in time of the test.

FIG. 6 shows an estimation of the autocorrelation function (acf) of theraw fasting BG-measurements (from the Case-Study). The acf is clearlyindicating that there is a dependency over time in the signal.

FIG. 7 shows a distribution histogram of the raw fastingBG-measurements, showing that they are approximately normal distributed.

FIG. 8 shows measurement differences between BG-meter 1 & 2, 1 & 3 and 2& 3 in the Case-Study.

FIG. 9 shows the periodogram of raw fasting BG-measurements (from theCase-Study). Most energy is within the low frequency band. Hence, higherfrequencies contain little or no useful information and may therefore bediscarded.

FIG. 10 shows the frequency response of the low-pass filter. Note thatthis cut-off frequency is a typical example.

FIG. 11 shows that filtering the fasting BG-samples for cut-offfrequencies between 0 and 1 generate residuals, or differences betweenraw samples and filtered samples. The mean value of the squaredresiduals, for each cut-off frequency, generates the curve in FIG. 11.This curve has a crossover break-point shown by two intersectingstraight lines, indicating a suitable cut-off frequency being chosen.

FIG. 12 shows a periodogram of fasting BG-samples being processedthrough a low-pass filter.

FIG. 13 shows raw systolic Blood Pressure samples together with itstrend generated by the same method of low-pass filtering as above.

FIG. 14 shows trends of fasting BG and physical activity, indicating acorrelation.

FIG. 15 shows correlation between BG- and RPP-trends (dotted line)generated by a rectangular moving window of 100 samples. Correlationsignificance (filled line, 1-P), should be >0.95 for significance.

FIG. 16 shows correlation between BG- and RPP-trends derivatives (dottedline) generated by a rectangular moving window of 100 samples.Correlation significance (filled line, 1-P), should be >0.95 forsignificance.

FIG. 17 shows that the system being identified can be represented by theblack-box approach.

FIG. 18 shows the result of low-pass filtered BG-prediction. Theprediction filter is in this example being updated every seventh day.

FIG. 19 shows trends of the Metabolic Performance Index and physicalactivity, indicating a correlation.

FIG. 20 shows a screen-dump of the first page of the computer programproduct.

FIG. 21 is a block diagram of a filter/trend device;

FIG. 22a is a block diagram of the FIG. 21 device having the secondprocessor;

FIG. 22b is a block diagram of the FIG. 21 device having the firstprocessor; and

FIG. 23 is a block diagram of an embodiment of the present invention.

DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 21 shows a block diagram for illustrating a filter/trend device,i.e., a block diagram for an apparatus for indicating a health-relatedcondition of a subject. This apparatus includes an input interface 20for receiving a raw sequence of samples of a biologic quantity relatedto the health condition of the subject, wherein the biological quantityhas a useful variation and a non-useful variation (arrow 21 in FIG. 21).

Depending on the specific implementation, the input interface obtainsthese samples of the biological quantity, which forms the raw sequence,by a manual input such as via a keyboard, by cable, radio, infra-red orother means from an analytical device, which for example analyses ablood sample to output a blood glucose value, blood pressure, heartrate, physical activity or any other biological quantity of interest, toan electronic buffer, memory or similar means within the input interface20. Thus, one obtains, at the output of the input interface 20, the rawsequence as a sequence of samples, which can be input to a filter device22 as indicated by an arrow 23 connecting block 20 and block 22.

Alternatively, or additionally, the raw sequence can also be input intoa first processor 24 for processing the raw sequence to obtain aprocessed sequence, which is, after being processed by the processor 24,filtered in the filter device 22.

The first processor 24 may include a predictor, an interpolator or anyother means, which is arranged to derive the processed sequence usingthe raw sequence output by block 20. In this connection, the firstprocessor can also include, as will be outlined later, a combiner forcombining two or more raw sequences to obtain a combined raw sequence,which can then be filtered by the filter device 22.

The filter 22 is arranged for filtering the raw sequence of samples orthe processed sequence of samples derived from the raw sequence ofsamples to obtain a filtered sequence. It is to be noted here that thefilter, which is preferably a low-pass filter, is configured forreducing the non-useful variation to obtain a filtered sequence, whichhas a stronger influence of the useful variation compared to theinfluence of the non-useful variation, or which can even be completelyeliminated.

The apparatus further comprises an output interface 25 for outputtingthe filtered sequence or an enhanced sequence derived from the filteredsequence, wherein the output interface is arranged to output at least anincrease indication, a decrease indication or a remain unchangedindication as a trend of the filtered sequence or the enhanced sequence,the trend being representative for a useful variation of thehealth-related condition of the subject. As it becomes clear from FIG.21, the output interface 25 processes the filtered sequence output bythe filter 22 directly as indicated by an arrow 26. Alternatively, theinventive apparatus further includes a second processor 27 for derivingthe enhanced sequence using the filtered sequence. Depending on acertain environment, the second processor 27 can include a combiner asshown in FIG. 22a or can include any signal conditioning means such asan amplifier, etc. to modify the filtered sequence for obtaining anenhanced sequence which is to be output.

With respect to the output interface 25, it is to be noted here that thetrend indication can be, of course, a graphical display as shown in FIG.20, showing a complete filtered or enhanced sequence. Alternatively, theoutput interface can also show simply the trend, by indicating anup-arrow or a coloured light or by other indicating means, when thetrend is going up, or by indicating a down-arrow or a different colouredlight or by other indicating means, when the trend is going down, or bydoing nothing or indicating any other sign to indicate that there is aremain-unchanged indication.

Naturally, this can also be done by a sensory perceptive interface forexample for the blind or deaf which outputs certain perceptiveindications for indicating an increase, a decrease or a remain unchangedsituation. Naturally, one can signal such an indication by mechanicalmeans such as a sound or strong vibration for an increase, a weekvibration for a decrease or a very weak vibration for a remain unchangedsituation. Alternatively, the frequencies of the vibrations can be madedifferent from each other for each indication. Alternatively, apart fromvibration indication means, one can also use any other mechanicalmarking such as raising a key so that the raised key can be felt by auser compared to the situation, in which the key is not raised.

FIG. 22a shows an embodiment of the second processor 27 of FIG. 21. Inthis embodiment, the combiner is the sample-wise combiner to forexample, multiply a sample of the filtered sequence of heart rate by asample of the filtered sequence of the blood pressure to obtain theenhanced sequence representing the filtered rate-pressure product.

FIG. 22b indicates an embodiment of the first processor 24, but for thecase, in which the blood pressure and the heart rate for example arecombined, i.e., sample-wise multiplied before filtering. This means thatthe FIG. 22b embodiment illustrates forming of a raw rate-pressureproduct, which is, subsequently, filtered by the filter 22 to reduce thenon-useful variations of the raw rate-pressure product.

FIG. 23 shows the inventive device in accordance with the invention,which includes a predictor 30 for providing, for a certain time, forwhich no sample of the first biologic quantity exists, an estimatedvalue of the first biological quantity. Preferably, a measurement valueof an invasive measurement is predicted using one or more measurementvalues of a non-invasive measurement, as is outlined in detail inconnection with FIG. 17 and FIG. 20. Depending on certain situations,the predictor can be a free-running predictor or a predictor, which isupdated in regular or irregular intervals.

The present invention builds on the simple concept that “knowledge givesmotivation” and encourages life-style improvement for the patient. Theinvention offers patient monitoring in a new way by the use of trendanalysis, based on well-proven and traditional patient measurements, andpresents new and improved ways of indicating patient status. Suchimproved information can be used by the patient and/or his doctor fortreatment planning and follow-up. The invention motivates and educatesthe patient by the use of performance feedback, so he can make progressin his life-style modification.

In type 2-diabetes related disease, it is current practice for thedoctor to inform the patient that a change of eating habits andlifestyle change is needed, but it is usually difficult for the patientto judge and comprehend the necessary level of change. It is oftendifficult to motivate the patient due to the “silent” nature of thisdisease. If the life-style modification is performed too aggressively,exhaustion and loss of motivation may result and the patient may giveup. On the other hand, if it is not performed seriously enough, it willnot have the desired effect. The benefit of the proposed invention isthat the appropriate level of lifestyle change is clearly indicated tothe patient in an intuitive way, thus avoiding discouragingover-efforts.

It is believed by the inventor that this, by the method indicated “justenough” level of approach, is the key to long-term motivation andsuccess of rehabilitation. This is achieved using new multi-parameterphysiological monitoring methods in combination with clear trendindications, thus encouraging self-control and rewarding the patient forgood behaviour for the effort made, and give an negative indication whenthe patient fails to progress. Such instantaneous indication ofperformance feedback is far superior to, and in strict contrast totraditional medical practice using very long-term “feedback” given bythe doctor on only a sparse per-visit basis.

The present invention describes a new method and/or a new device thatneeds a minimum of patient engagement and effort, where some patientparameters are frequently sampled, once a day or even once a week andother parameters are sampled less frequently. The frequently sampledparameters may easily be performed at bedside in the morning and noequipment or tools needs to be carried around during the day. The lessfrequently sampled patient parameters may be performed for example atthe clinic.

Frequently sampled physiological patient parameters in a densely orsparsely manner, equidistant or non-equidistant sampled, may consist of:

Blood glucose

Physical activity

Blood pressure

Heart rate

Body temperature

Body weight

Body Mass Index

Substantially less frequently sampled patient parameters can consist of:

HbA1c

Insulin

Lipids

Albumin levels

Other related parameters of interest

When assessing blood-glucose levels at home it is important that theanalytic variability of the measuring instrument is low andsubstantially less than the biological variability of the patient. Elsethe measurement will be meaningless. Unfortunately some personal-typeblood-glucose meters have an unacceptably high analytic variability,making them less reliable and useful for accurate blood-glucosemeasurements. However some commercially available low-cost personal-typeblood-glucose meters are found to be sufficiently accurate for reliablemeasurements of for example fasting BG, provided appropriate postprocessing of the data is performed. On the other hand if higheraccuracy is wanted, for example two or more consecutive measurementswithin minutes can be performed, and subsequently averaged in apost-process. Multiple BG-meters can also be used in parallel to reducevariability and the results averaged. This can preferably be performedin clinical research when high accuracy is needed and has been used inthe research to verify the proposed invention.

Due to the strong biologic variability of the blood glucose level incombination with some analytic variability of the blood glucosemeasuring instrument, substantial data scatter is experienced, makingthe noisy signal difficult to interpret. See FIG. 4, that demonstrates atypical fasting BG sequence spanning over approximately 10 months. Notethe difficulty in diagnosing a patient accurately as the data is verynoisy, thus showing high biologic variability. The data over time isscattered over a wide range, thus the patient BG is spanning from normalto diabetic values. If one counts the number of days that satisfy eachWHO criteria for our case-study patient, we get an interesting graph,see FIG. 5. 37% of the 257 days evaluated the patient is fully normal.57% of the days he has Impaired Fasting Glucose (IFG). 7% of the days hehas manifest diabetes.

According to the above strong variability of BG, the inventor stronglybelieves that current diabetes criteria results in sub-optimum diagnosisand therefore needs to be revised. In order to make BG interpretationmore accurate, low-pass filtering of multiple BG data is necessary.However, it is important not to filter the data too excessively, as thiswill reduce short-term variations and blunt the details of variation.Optimum filtration and avoidance of over-filtration can be obtained byresidual analysis described later.

Although measurements of BG seem to be extremely noisy it cannot becharacterized as white noise. For clarification one can take a look atthe estimated autocorrelation function (acf) where dependence is obvious(see FIG. 6 where the estimated acf is based on the inventors long-termfasting BG). The measurements, in this case study, are approximatelynormal distributed (see FIG. 7). If the measurements had largervariations it would most certainly be lognormal distributed.

Because of the inventor's measurement strategy, using three high qualityBG-meters of the same brand, one can calculate the analytical error.This is being done by comparison of two BG meters at a time, whichgenerates three approximately normal distributed cases with a standarddeviation around 0.35 mmol/L (see FIG. 8). The data-series BG1, BG2 andBG3 generated by the three meters are independent of each other andN(m,σ). BG is the arithmetic mean described by

$\begin{matrix}{\overset{\_}{BG} \in {N\left( {m,\frac{\sigma}{\sqrt{n}}} \right)}} & (1)\end{matrix}$where the standard deviation σ is approximately the same for eachcomparison, which are three to the number (n). By the use of thestatistical rule that the variance of two normal distributed data-seriesare additive, we get√{square root over (2σ²)}≈0,35  (2)

Hence, the standard deviation of the mean values of the three metersused in the case study is approximately 0.14 mmol/L.

To achieve a clear trend presentation of the noisy data it is necessaryto process the data with a low-pass filter, which can be done throughspectral analysis. In FIG. 9 the periodogram is shown where one can seethat most energy is found within a low frequency band. The low-passfiltering is being processed by multiplication in the frequency domain

$\begin{matrix}{{S_{LP}\left( {\mathbb{e}}^{j\;\omega} \right)} = {{H\left( {\mathbb{e}}^{j\omega} \right)}\frac{1}{N}{\sum\limits_{t = 1}^{N}{{{BG}(t)}{\mathbb{e}}^{{- {j\omega}}\; t}}}}} & (3)\end{matrix}$where His a FIR low-pass filter in the frequency domain (see FIG. 10 forfrequency response for random picked cut-off frequency) and BG(t) rawmeasurements which are Fourier Transformed. S_(LP) is then transformedback to the time domain via the inverse Fourier transform. Hence,residuals can be generated.ΔBG _(fd)(t)=BG(t)−BG _(LP(fd))(t)  (4)

For a certain cut-off frequency fd between 0 and 1 (discrete frequency).When fd increases from 0 to 1 we can calculate the mean of the squaredresiduals, where N is the length of the residual vector for each valueof fd.

$\begin{matrix}{\frac{1}{N}{\sum\limits_{t = 1}^{N}{\Delta\;{{BG}(t)}_{0}^{2}\mspace{14mu}\ldots\mspace{14mu}\frac{1}{N}{\sum\limits_{t = 1}^{N}{\Delta\;{{BG}(t)}_{1}^{2}}}}}} & (5)\end{matrix}$

This will generate a curve describing the behavior of the residuals fordifferent fd (see FIG. 11). To find the most suitable cut-off frequencyone should choose the frequency for the intersection in FIG. 11. Themain purpose of the straight lines in FIG. 11 is to clarify the positionof the residual-curve break. The same residual analysis can be appliedto other biological measurements and signals. When the so designedlow-pass filter processes the data, non-wanted high frequencies will beremoved (see FIG. 12) by multiplying the zero-padded Fourier transformsof the LP-filter and the BG measurements.

The result of the LP-filtering using the chosen cut-off frequency in thefrequency domain and time domain, are shown in FIG. 12 and FIG. 4respectively.

As an alternative one can perform similar filtering in the time-domainusing convolution. Other types of low-pass filters may also be used bythose skilled in the art.

Blood pressure can be measured at both arms and then low-pass filteredin order to reduce variance. Blood pressure can also be measured at thewrist, finger or other places. The Pulse-Wave-Transition-Time (PWTT)estimation can also be used to measure blood pressure. This estimatesthe blood pressure by measuring the pulse-wave transition time, startingfrom when the heart creates for example a EKG R-wave, to when thepulse-wave creates a light transmission difference due to changing bloodpulse density, detected at a finger by a plethysmograph. In addition,from the systolic, diastolic and pulse data, it may be an advantage tocalculate the Mean Arterial Pressure (MAP) and Pulse Pressure (PP) andpresent this data graphically.

In a similar manner, physical activity data is usually scattered due tolarge variations in daily activity or due to approximate estimations. Itis therefore convenient to low-pass filter such data over time in asimilar manner as above, as this makes the physical activity data easierto interpret. Physical activity can simply be estimated on an intensityscale where such scale can comprise the following grading of dailyactivities:

Very light (resting, reading, sitting, driving etc)

Light (walking, sweeping, playing piano, slow walk)

Moderate (fast walk, easy jogging, easy bicycling, skating, light weighttraining)

Hard (swimming, running, intense jogging, bicycle race, football,basketball etc)

Very hard (boxing, rowing, mountain climbing, intense weight training)

For more accurate estimation the MET (metabolic equivalent) can be used.1 MET is equivalent to resting energy expenditure and light activity is<3 METS, moderate 3-5.9 METS, hard 6-8.9 METS or very hard >9 METSactivity. MET activity tables are available to simplify calculation ofcalories burned (kcal), which is carried out by multiplying MET-value,weight and time elapsed. A cost-effective way of estimating physicalactivity is to use a pedometer. The activity data collected in theexample graphs of the invention is using a pedometer that is used incombination with a built-in timer to calculate the approximate caloriesburned during the day or physical activity performed. It is practical toindicate energy expenditure as calories burned, as this is a commonlyused and understandable term.

The heart rate data is also scattered due to large variations day today. It is therefore convenient to low-pass filter such data in asimilar manner as above, as this makes the heart rate data easier tointerpret.

In an additional embodiment of the invention, systolic and diastolicblood pressure and heart rate is measured on a preferably daily basis inboth arms. The data from both arms can then be averaged and low-passfiltered to reduce variability. The product of the systolic bloodpressure and heart rate is calculated to obtain the Rate PressureProduct (RPP) in order to estimate the physical condition of thepatient. RPP=Systolic BP*Heart rate/100. In addition to approximatelyindicate the oxygen utilization of the heart, the RPP reveals thepresence of stimulating drugs like caffeine, nicotine, cocaine andamphetamine as well as mental and emotional stress. Thus the inventorteaches that the RPP is an important parameter to evaluate together withBG to establish the overall health-related condition of the patient. Toachieve a trend presentation of RPP, as well as its separate componentsthemselves, we can use a similar low-pass filter method, which producedthe BG-trend. It may also be valuable for the physician to evaluate anyblood pressure differences between the left and right arm, according toa separate long-term average of each arm.

In a similar manner, the morning blood pressure data at rest isscattered due to large variations day to day and in addition due toanalytic variability. Having the patient or doctor to make single-spotblood pressure measurements does not seem very meaningful according tothe large noise level that also exists in the BP-data. It is thereforenecessary to low-pass filter such data, as this makes the blood pressuredata more accurate and easier to interpret, see FIG. 13.

By simultaneously comparing the indicated data from the physicalactivity and BG-level filtered in an abovementioned way, it can be seenthat an improvement in physical activity results in reduction in bloodglucose level such as the blood glucose level is inversely proportionalto physical activity. However, extreme physical activity can undercertain conditions actually have the opposite effect, raising the bloodglucose level. Thus by simultaneously presenting such data indicated forexample graphically to the patient, he or she can easily adopt hiseffort of physical activity and other life style related efforts tosuite a predefined target goal. This can now be achieved in an accurateand intuitive way, not needing to over-exaggerate the effort made, butinstead simply working towards the blood glucose, RPP and activitytarget goal in a timely manner day-by-day as indicated by the progressin the graphics, see FIG. 14. It should be noted that new interestingde-correlations can also be observed in the graphs, see FIG. 15. Forexample when the patient has a flu or a virus infection, the BG valuemay rise unexpectedly and independently of the improved physicalActivity. When increasing the physical activity, the BG value may alsorise while the RPP decrease and the correlation become negative. Or whenthe patient encounters a stressful situation, the RPP may rise more thanthe BG. One could thus suspect a negatively correlated event under suchconditions. Thus calculating the time-windowed correlation coefficientbetween RPP, BG and Activity and indicating this in the graphs, offers anew interesting indicator of patient status, and new conclusions can bedrawn by an experienced user upon such negative correlation indications.

In another embodiment of the invention a new method is presented wherethe inventor has discovered that the RPP dynamics correlates well withthe BG dynamics and in inverse proportion to the level of physicalactivity and thus RPP may be used to predict BG fluctuations anddynamics, see FIG. 15 and FIG. 16.

In yet another embodiment of the invention the RPP can be used togetherwith a predicting filter to calculate a surrogate measure for daily BG.This new BG prediction method can advantageously be used when it is notpossible, impractical or felt painful and inconvenient by the patient tosample blood. Under such circumstances, BG measurements may be used onlyinitially of the treatment or intervention period to calibrate the RPPpredictor against the BG values. After such calibration is performed,the patient may revert to RPP measurements only, and take BGmeasurements for example only when visiting the doctor. In yet anotherembodiment of the invention, as described below, the BG-predictionfilters are being updated on a sparse basis, for example once a week.Thus, initial training of the prediction filter first requires adata-sequence of densely sampled measurements. The length of thistraining sequence may be for example from one week to a month.Subsequently the prediction filter can be updated sparsely. The proposedprediction methods can be used to predict any signal ×1 out of thesignal ×2, if a correlation is detected between ×1 and ×2 (note that ×2may be a combination of several measurement quantities).

Thus both BG and RPP can be used as an important indicators of improvedself-control and life-style change in type 2-diabetes related disease.RPP shows a correlation to BG, and in particular under transitionalphases of lifestyle changes, such as changing from sedentary lifestyleto a more active lifestyle or between varying intensity periods ofphysical activity. Thus the derivative of BG and derivative of RPP havea strong correlation (see FIG. 16). Under such circumstances the trendsof both RPP and BG parameters change in a similar manner indicating highcorrelation. Under steady-state condition when the human is in“metabolic equilibrium” the correlation between RPP and BG may be lessprominent due to excessive noise in the data from other metabolicprocesses. Thus the prediction filter is used to predict daily BG datafrom densely sampled RPP data. It needs to be mentioned that estimatingBG by the use of the RPP is an economic and painless method as no, oronly few blood glucose sticks or finger-pricking lances needs to beused. Measuring blood pressure for calculating the RPP does thereforenot need any consumables as BG testing does. The proposed predictionmethod can be used also for other and future BG measurement methodswhere such methods are deemed cumbersome, impractical or uneconomicaletc. Such methods may consist or measuring BG values from tear-liquid,from saliva or from instruments in contact with the skin etc.

The present invention predicts daily BG from the RPP at a high accuracyfrom the use of only sparsely sampled blood samples. The prediction maybe proceeded by two different approaches, ARX and FIR-Wiener. Asmentioned before, prediction methods require a sequence of data fortraining. Such sparsely sampled BG values are used to update an advancedfilter predictor. Thus for the patient that has traumatic sensationsfrom blood sampling or finger pricking, such painful activities can bereduced to for example one sample per week and still accurate daily BGvalues can be predicted by the predictor from the sparingly sampled BGvalues. The system is identified using transfer functions together withBG, an input signal x and white noise. x can be a vector of one variableor a matrix of several variables. Examples of variables can bemeasurements such as Rate Pressure Product, Systolic Blood Pressure,Diastolic Blood Pressure, Pulse, Mean Arterial Pressure, Pulse Pressureor Physical Activity. This identification can be done since we areassuming that BG and x are partly affected by the same underlyingparameters. Among these parameters we find for example physicalactivity, food habits, stress, virus and overweight. Therefore, we canpresent the system by the following hypothesis.BG(t)=G(θ,q)×(t)+H(θ,q)e(t)  (6)which is a description of a linear system where the noise part e(t) is astochastic white noise with E[e(t)]=0. In a wider sense, the system canbe described by the principles of a black box (see FIG. 17). G, and Haretransfer functions and θ is a vector containing thepolynomial-coefficients. Moreover, q is the shift operator. It is mostpreferable to use Rate Pressure Product since it has the highestcorrelation with BG. Therefore; the example below is using BG and RPPdata.

An important pre-process when a system is being identified, is tosubtract the mean value. This is given by

$\begin{matrix}{{\overset{\_}{BG} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{BG}(t)}}}},{\overset{\_}{RPP} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{RPP}(t)}}}}} & (7)\end{matrix}$where N is the number of measurements. There are several ways toestimate the transfer functions G(θ,q) and H(θ,q), where models likeARX, ARMAX, OE and Box-Jenkins can be mentioned. In this case ARX isdiscussed, which provides a straightforward prediction algorithm calledlinear regression. Box-Jenkins is the most complex model which the othermodels are being special cases of Tests have shown minimum amount ofdifferences between the different model-approaches.

The ARX-model can be written asBG(t)+a ₁ BG(t−1)+ . . . +a _(n) _(a) BG(t−n _(a))=b₁ RPP(t−1−nk)+ . . .+b _(n) _(a) RPP(t−n _(b) −nk+1)+e(t)  (8)where the polynomial-coefficients can be collected and written asθ=[a ₁ . . . a _(n) _(a) b ₁ . . . b _(n) _(a) ]^(T)  (9)

Furthermore, equation 2 can be rewritten as

$\begin{matrix}{{{{A(q)}{{BG}(t)}} = {{{B(q)}{{RPP}\left( {t - {nk}} \right)}} + {e(t)}}}{where}} & (10) \\{{{G\left( {q,\theta} \right)} = \frac{B(q)}{A(q)}}{and}{{H\left( {q,\theta} \right)} = \frac{1}{A(q)}}} & (11)\end{matrix}$nk is the delay.

Given the optimal elements in the vector θ, old BG- and RPP values, itis possible to predict BG. The prediction is being calculated withknowledge of θ and the regression vector φ, containing old BG and RPPvalues.φ(t)=[−BG(t−1) . . . −BG(t−n _(a))RPP(t−nk) . . . RPP(t−n _(k) −n _(b)+1)]^(T)  (12)

Note that the noise term e(t) is not a member of φ. Furthermore, theproduct of θ and φ provides the prediction{circumflex over (B)}G(t|θ)=θ^(T)φ(t)  (13)

In the example the predictor is designed as a one-step predictor, andbecomes adaptive as it retrains for every prediction. Other step lengthsof predictors and other types of predictors can be used by those skilledin the art.

For each calculation of θ at the time t−1, a guess or a prediction ofBG(t) will be produced. Hence, at the time t it is possible to carry outthe prediction errorε(t,θ)=BG(t)−{circumflex over (B)}G(t|θ)  (14)

For a training sequence of the length N we get the quadratic criteria

$\begin{matrix}{{V_{N}(\theta)} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{ɛ^{2}\left( {t,\theta} \right)}}}} & (15)\end{matrix}$

It is therefore straight forward to pick the t 9, which gives

$\begin{matrix}{{\hat{\theta}}_{N} = {\arg\;{\min\limits_{\theta}{V_{N}(\theta)}}}} & (16)\end{matrix}$

(“arg min” is the minimized argument)

We have the prediction errorε(t,θ)=BG(t)−θ^(T)φ(t)  (17)

Hence, the quadratic criteria (11) can be written as

$\begin{matrix}{V_{N} = {{\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{{BG}(t)} - {\theta^{T}{\varphi(t)}}} \right)^{2}}} = {{{\frac{1}{N}{\sum\limits_{t = 1}^{N}{{BG}^{2}(t)}}} - {\frac{1}{N}{\sum\limits_{t = 1}^{N}{2\theta^{T}{\varphi(t)}{{BG}(t)}}}} + {\frac{1}{N}{\sum\limits_{t = 1}^{N}{\theta^{T}{\varphi(t)}{\varphi^{T}(t)}\theta}}}} = {{\frac{1}{N}{\sum\limits_{t = 1}^{N}{{BG}^{2}(t)}}} - {2\theta^{T}f_{N}} + {\theta^{T}R_{N}\theta}}}}} & (18) \\{\mspace{20mu}{where}} & \; \\{\mspace{20mu}{{f_{N} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{\varphi(t)}{{BG}(t)}}}}}\mspace{20mu}{and}}} & (19) \\{\mspace{20mu}{R_{N} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{{\varphi(t)}{\varphi^{T}(t)}}}}}} & (20)\end{matrix}$

If R_(N) is invertible, the formula can be written as

$\begin{matrix}{V_{N} = {{\frac{1}{N}{\sum\limits_{t = 1}^{N}{{BG}^{2}(t)}}} - {f_{N}^{T}R_{N}^{- 1}f_{N}} + {\left( {\theta - {R_{N}^{- 1}f_{N}}} \right)^{T}{R_{N}\left( {\theta - {R_{N}^{- 1}f_{N}}} \right)}}}} & (21)\end{matrix}$

The last part of (19) is always zero ifθ={circumflex over (θ)}_(N) =R _(N) ⁻¹ f _(N)  (22)and because R_(N) is positive definite, this provides a minimum. Hence,the optimal minimized value of V_(N)(θ) is given when equation (22) isfulfilled, because the rest of the terms are independent of θ. Toimprove this predictor one can use the information of residuals, whichwill be available when a true BG-sample is being taken. This residualcan be weighted exponentially in order to be added to forthcomingpredictions for improved amplitude tracking.

As an example, FIG. 18 show the results of the predictor when it isbeing updated with true BG-samples only every 7:th day. In anotherembodiment of the invention the predictor may free-run and updated onlyinitially by a short BG-sequence.

As another prediction example one can implement a FIR-Wiener filter,which is a powerful predictor suppressing noise optimally. A variabledescribing future BG-samples can be written as (of course BG is just anexample in this matter, one can replace BG with some other sparsesampled variable being correlated with the densely sampled variable).Hence, we create the predicted value BG as.x _(k) =BG(n+k)  (23)

We create a vector containing BG- and RPP-measurements.y=[BG(n) . . . BG(n−tM)RPP(n+k) . . . RPP(n−T+k)]  (24)where t is the number of old BG-values in and M is the testing intervalof BG. T represents old values of RPP while k is the number of stepsbeing predicted (k<M).

Further, we estimate a matrix containing auto-correlation functions andcross-correlation functions. In order to calculate these estimations wecan use a sequence of known measurements, as a training sequence.{circumflex over (R)} _(yy) =E[y ^(T) y]  (25)

We also estimate the cross-correlationr _(xy) =E[x _(k) y ^(T)]  (26)

Now the filter can be created as, (one for each prediction, k is anindex of the prediction step)h _(k) ={circumflex over (R)} _(yy) ⁻¹ r _(xy)  (27)

Which leads to the predicted value{circumflex over (B)}G(n+k)=h _(k) y ^(T)  (28)

Analysis of the time series data for relevance should be performed anddata dropouts or outliers above a threshold can be substituted usingaverages of neighbouring data. This is most important, as it is normalto sometimes forget to take measurements or sometimes make errors inmanual interpretation of measurements. Long data dropouts may need to beinterpolated in such situation when the subject has forgotten his deviceor when gone for a holiday etc. The linear interpolation is also analternative to the linear regression prediction method. For example, ifBG measurements are being taken once every week, the vector of knownBG-data is a down sampled version of an every day sampled BG vector.This can be described asBG _(M)(n)=BG(nM)  (29)for any interpolation interval M days (or samples). The linearinterpolation is then carried out by applying a straight line based onM−1 samples between the elements in BG_(M). As an example, FIG. 18 showsthe results of the interpolator with true BG-samples every 7:th day.Linear interpolation also can be carried out for non-equidistant dateswith missing data.

Further, in another embodiment of the invention it may be preferable toautomatically switch between linear interpolation and prediction, basedon the sequence of missing data. The appropriate point in time of theswitch-over may be determined by residual analysis of past known data.The residuals are generated from two cases: Case 1 where linearinterpolated data in a certain sequence are compared with raw data. Case2 where predicted data in the very same sequence are compared with rawdata. This is of course being done in the same interval/sequenceproviding useful comparison between the two cases. The goal is to keepthe residuals as small as possible and therefore the switchover pointmay be determined where the mean of the squared residuals from two casesintersect.

The sum or factorisation of BG and RPP may be used as a metabolicperformance indicator called the Metabolic Performance Index (MPI) bythe inventor, an indicator that may span a number of abnormalities anddisease, thus a clear indicator for the promotion of self control andlife-style change in type 2-diabetes related disease, see FIG. 19. Earlyindications show that the MPI indicator may also be used with advantagein sports training events for athletes etc.

It is believed by the inventor that a metabolic monitoring andindication device according to the proposed invention will be a veryvaluable asset to the patient for self management as well as a new toolfor the physician to clearly and accurately assess and follow up patientstatus, and as such can be used as a valuable treatment tool. Ascreen-shot of a version of the software product is shown in FIG. 20. Itis also believed that this multiparameter metabolic monitoring andindication device can be used to monitor physical status and progress ofany human such as a sports athletic like a runner or swimmer etc, andfor any mammalian such a race horse or racing dog, where its trainer canmake positive use of the output data to guide further training andimprove performance.

As an alternative to new hardware development, standard proventechnologies and mass-produced consumer medical monitoring instrumentsmay be used for data collection where a computer program product and acomputer (desk-top, lap-top, palm-top or smart-phone) can be used tocollect, download, analyse and present the information in a practicaland intuitive way. In addition, intelligent blood-glucose monitors canbe built or intelligent combination apparatus of blood-glucose and bloodpressure monitor including a microprocessor and accelerometer forphysical activity and a screen for display. The present invention can beimplemented both in software, hardware chips and DSPs, for various kindsof use, for computation, storage and/or transmission of the signals,analogue or digital.

The described embodiments are merely illustrative for the principles ofthe present invention. It is understood that modifications andvariations of the arrangements and the details described herein will beapparent to others skilled in the art. It is the intent, therefore, tobe limited only by the scope of the impending patent claims and not bythe specific details presented by way of description and explanation ofthe embodiments herein.

The invention claimed is:
 1. An apparatus for indicating ahealth-related condition of a subject, comprising: an analytical devicefor sampling a biological quantity as part of a measurement method,where the biological quantity is related to the health-related conditionof the subject; an input interface for receiving a raw sequence ofsamples of the biological quantity from the analytical device; aprocessor for processing the raw sequence of samples to obtain aprocessed sequence, the processor comprising an interpolator forproviding data on an interpolated sample for a missing sample of thebiological quantity using one or more samples at a preceding timeinstant or one or more samples at a subsequent time instant to obtain aninterpolated sequence representing samples of the raw sequence and theinterpolated sample; a low-pass filter for filtering the processedsequence to obtain a filtered sequence, the filtered sequence having auseful variation and a reduced non-useful variation compared to theprocessed sequence before filtering, wherein the low-pass filter has acut-off frequency; a cut-off frequency calculator being arranged todetermine the cut-off frequency of the low-pass filter, by: for aplurality of different cut-off frequency values, low-pass filtering thesamples of the raw sequence to obtain filtered test signals; for eachfiltered test signal, deriving a residual value based on the differenceof the samples of the raw sequence and the filtered test signal toobtain a residual representation; and based on the residualrepresentation, determining the cut-off frequency of the low-pass filteradapted to the raw sequence of samples; and an output interface forprocessing the filtered sequence or for processing an enhanced sequencederived from the filtered sequence and for outputting an indication ofthe health-related condition of the subject based on the filteredsequence or the enhanced sequence derived from the filtered sequence. 2.Apparatus in accordance with claim 1, further comprising: a secondprocessor for deriving the enhanced sequence from the filtered sequence,wherein the second processor comprises a combiner or a signalconditioning means such as an amplifier for modifying the filteredsequence.
 3. Apparatus in accordance with claim 2, wherein the inputinterface is configured for receiving a further raw sequence of adifferent biological quantity derived by a further measurement method,wherein the filter is configured for filtering the further raw sequenceto obtain a further filtered sequence, and wherein the second processorcomprises a combiner for combining the filtered sequence and the furtherfiltered sequence to derive the enhanced sequence.
 4. Apparatus inaccordance with claim 3, in which the measurement method is a blood orplasma glucose measurement, and in which the further measurement methodis a heart rate measurement, a blood pressure measurement or a methodfor obtaining a product of heart rate and blood pressure.
 5. Apparatusin accordance with claim 3, in which the combiner is arranged forperforming a sample-wise multiplication.
 6. Apparatus in accordance withclaim 1, in which the biological quantity is a blood glucose level, ablood lipid level or a blood insulin level of the subject.
 7. Apparatusin accordance with claim 1, in which the health-related condition is adiabetes-related or glucose-related or insulin-related metabolicdisorder.
 8. Apparatus in accordance with claim 1, in which thebiological quantity is a blood glucose level.
 9. Apparatus in accordancewith claim 1, in which the output interface is configured for outputtingat least an increase indication, a decrease indication or a remainunchanged indication as a trend of the filtered sequence or the enhancedsequence, the trend being representative of a useful variation of thehealth-related condition of the subject.
 10. Apparatus in accordancewith claim 9, in which the output interface is arranged to indicate thetrend by an acoustic indicator, an optical indicator or a mechanicalindicator, so that the decrease indication, the increase indication orthe remain unchanged indication are acoustically, optically ormechanically different from each other.
 11. Apparatus in accordance withclaim 9, in which the output interface is arranged to derive and outputthe trend from an actual value of the filtered sequence or the enhancedsequence, and a timely preceding value of the filtered sequence or theenhanced sequence.
 12. Apparatus in accordance with claim 1, in whichthe residual value for a filtered test signal is a mean squareddifference of the samples of the raw sequence and corresponding samplesof the filtered test signal.
 13. Apparatus in accordance with claim 1,in which the cut-off frequency calculator is arranged to determine thecut-off frequency based on the residual representation by: determining afirst line representing a trend of residual energies for relativelylower cut-off frequency values; determining a second line representing atrend of residual energies for relatively higher cut-off frequencyvalues; and determining an intersection point of the first line and thesecond line, wherein determining the cut-off frequency adapted to theraw sequence of samples based on the residual representation comprisesselecting the cut-off frequency of the low-pass filter corresponding tothe intersection point.
 14. Apparatus in accordance with claim 1,wherein deriving the residual value for each filtered test signalcomprises deriving residual energy for each filtered test signal.
 15. Amethod for indicating a health-related condition of a subject,comprising: receiving, at an input interface, a raw sequence of samplesof a biological quantity from an analytical device for sampling thebiological quantity as part of a measurement method, where thebiological quantity is related to the health-related condition of thesubject; processing the raw sequence of samples using a processor toobtain a processed sequence, comprising providing data on aninterpolated sample for a missing sample of the biological quantityusing one or more samples at a preceding time instant or one or moresamples at a subsequent time instant to obtain an interpolated sequencerepresenting samples of the raw sequence and the interpolated sample;determining signal-adaptively, on the basis of the raw sequence ofsamples and using a cut-off frequency calculator, a cut-off frequency ofa low-pass filter, by: for a plurality of different cut-off frequencyvalues, low-pass filtering the samples of the raw sequence to obtainfiltered test signals; for each filtered test signal, deriving aresidual value based on the difference of the samples of the rawsequence and the filtered test signal to obtain a residualrepresentation; and based on the residual representation, determiningthe cut-off frequency of the low-pass filter adapted to the raw sequenceof samples; filtering the processed sequence using the low-pass filterwith the determined cut-off frequency, to obtain a filtered sequence,the filtered sequence having a useful variation and a reduced non-usefulvariation compared to the processed sequence before filtering;optionally deriving, using a further processor, an enhanced sequencefrom the filtered sequence; and processing the filtered sequence orprocessing the enhanced sequence and outputting, at an output interface,an indication of the health-related condition of the subject based onthe filtered sequence or the enhanced sequence.
 16. A non-transitorydigital storage medium having stored thereon a computer program having aprogram code for performing a method for indicating a health-relatedcondition of a subject, the method comprising: receiving, from ananalytical device for sampling of a biological quantity related to thehealth-related condition of the subject as part of a measurement method,a raw sequence of samples of the biological quantity; processing the rawsequence of samples to obtain a processed sequence, comprising providingdata on an interpolated sample for a missing sample of the biologicalquantity using one or more samples at a preceding time instant or one ormore samples at a subsequent time instant to obtain an interpolatedsequence representing samples of the raw sequence and the interpolatedsample; determining signal-adaptively, on the basis of the raw sequenceof samples, a cut-off frequency, by: for a plurality of differentcut-off frequency values, low-pass filtering the samples of the rawsequence to obtain filtered test signals; for each filtered test signal,deriving a residual value based on the difference of the samples of theraw sequence and the filtered test signal to obtain a residualrepresentation; and based on the residual representation, determiningthe cut-off frequency adapted to the raw sequence of samples; low-passfiltering the processed sequence in accordance with the determinedcut-off frequency, to obtain a filtered sequence, the filtered sequencehaving a useful variation and a reduced non-useful variation compared tothe processed sequence before filtering; optionally deriving an enhancedsequence from the filtered sequence; and processing the filteredsequence or processing the enhanced sequence and outputting anindication of the health-related condition of the subject based on thefiltered sequence or the enhanced sequence, when the computer program isrunning on a computer.